Abstract: Cooperation is a vital component of a social network. Two of its forms are altruism, in which agents work together for the good of the team, and selfish cooperation, in which agents work together for their own benefit. This talk describes a mathematical framework, based upon classical cooperative game theory, that encapsulates these two kinds of cooperation. One can easily apply this framework to social networks by using various graph invariants to measure the utility of a network's configuration. We discuss the resulting metrics of altruism and selfish cooperation for a few particular invariants, and demonstrate some examples that show how this framework can expand our understanding of cooperation within social networks. We conclude by looking at pursuit and evasion simulations. These simulations typically involve two teams with a continuously varying communications network. Preliminary work indicates how our theoretical framework might be used to construct more cooperative teams within these games. We expect that the resulting techniques will be applicable to a wide range of scenarios.